import logging
import math
import os
import sys
import time
from dataclasses import dataclass, field
from itertools import chain
from typing import List, Optional

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset, load_from_disk  # type: ignore[attr-defined]
from instruction_dataset_utils import InstructionDataset
from peft import LoraConfig, TaskType, get_peft_model
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_CAUSAL_LM_MAPPING,
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    HfArgumentParser,
    TrainerCallback,
    default_data_collator,
    is_torch_tpu_available,
    set_seed,
)
from transformers.integrations import TensorBoardCallback
from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaForCausalLM
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import EvalPrediction, get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version

from atorch.trainer import STREAMING_CKPT_DIR, AtorchArguments, AtorchTrainer
from atorch.utils.meta_model_utils import init_empty_weights_with_disk_offload

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")

logger = logging.getLogger(__name__)


MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
            )
        },
    )
    model_type: Optional[str] = field(
        default=None,
        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
    )
    config_overrides: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        },
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        },
    )
    torch_dtype: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
                "dtype will be automatically derived from the model's weights."
            ),
            "choices": ["auto", "bfloat16", "float16", "float32"],
        },
    )
    low_cpu_mem_usage: bool = field(
        default=False,
        metadata={
            "help": (
                "It is an option to create the model as an empty shell, then only materialize its parameters when the"
                " pretrained weights are loaded. set True will benefit LLM loading time and RAM consumption."
            )
        },
    )
    ignore_mismatched_sizes: bool = field(
        default=False, metadata={"help": "If True, will set ignore_mismatched_sizes=True when calling from_pretrained."}
    )
    use_bettertransformer_kernels: bool = field(
        default=False,
        metadata={
            "help": (
                "setting 'use_bettertransformer_kernels' will enable using of Flash Attention or Xformer "
                "memory-efficient kernels based on the hardware being used. This would speed up fine-tuning."
            )
        },
    )
    use_flash_attention_2: bool = field(
        default=False, metadata={"help": "Requires 'transformers>=4.34.0' and 'flash-attn>=2.0'"}
    )

    def __post_init__(self):
        if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
            raise ValueError(
                "--config_overrides can't be used in combination with --config_name or --model_name_or_path"
            )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    dataset_path: Optional[str] = field(default=None, metadata={"help": "A dir containing dataset with .arrow format."})
    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )
    streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
    block_size: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "Optional input sequence length after tokenization. "
                "The training dataset will be truncated in block of this size for training. "
                "Default to the model max input length for single sentence inputs (take into account special tokens)."
            )
        },
    )
    overwrite_cache: bool = field(default=False, metadata={"help": "Overwrite the cached training and evaluation sets"})
    validation_split_percentage: Optional[int] = field(
        default=5,
        metadata={"help": "The percentage of the train set used as validation set in case there's no validation split"},
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    keep_linebreaks: bool = field(
        default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
    )
    evaluate_script: str = field(
        default="accuracy", metadata={"help": "Local evaluate script. Downloaded by evaluate.load()"}
    )

    def __post_init__(self):
        if self.streaming:
            require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")

        if (
            self.dataset_name is None
            and self.train_file is None
            and self.validation_file is None
            and self.dataset_path is None
        ):
            raise ValueError("Need either a dataset name or a training/validation file or a dataset path.")
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


@dataclass
class PeftArguments:
    peft_type: Optional[str] = field(default=None, metadata={"help": "Whether use peft"})
    lora_r: int = field(default=8, metadata={"help": "Lora attention dimension."})
    lora_alpha: int = field(default=16, metadata={"help": "The alpha parameter for Lora scaling."})
    lora_dropout: float = field(default=0.05, metadata={"help": "The dropout probability for Lora layers."})
    lora_target_modules: List[str] = field(
        default_factory=lambda: ["q_proj", "v_proj"], metadata={"help": "The names of the modules to apply Lora to."}
    )
    peft_task_type: str = field(default=TaskType.CAUSAL_LM, metadata={"help": "Peft task type."})

    def __post_init__(self):
        peft_task_type_choices = [
            str(TaskType.SEQ_CLS),
            str(TaskType.SEQ_2_SEQ_LM),
            str(TaskType.CAUSAL_LM),
            str(TaskType.TOKEN_CLS),
        ]
        if self.peft_task_type not in peft_task_type_choices:
            raise ValueError(f"peft_task_type {self.peft_task_type} not in {peft_task_type_choices}")


@dataclass
class LlamaTrainingArguments(AtorchArguments):
    enable_torch_profiler: bool = field(default=False, metadata={"help": "If passed, use torch.profiler.profile"})


def get_config(model_args):
    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "use_auth_token": True if model_args.use_auth_token else None,
        "ignore_mismatched_sizes": model_args.ignore_mismatched_sizes,
    }
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, trust_remote_code=True, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True, **config_kwargs)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")
    return config


def get_tokenizer(model_args):
    tokenizer_kwargs = {
        "cache_dir": model_args.cache_dir,
        "use_fast": model_args.use_fast_tokenizer,
        "revision": model_args.model_revision,
        "use_auth_token": True if model_args.use_auth_token else None,
    }
    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, trust_remote_code=True, **tokenizer_kwargs)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.model_name_or_path, trust_remote_code=True, **tokenizer_kwargs
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )
    return tokenizer


def get_model(model_args, training_args, peft_args, config):

    if model_args.model_name_or_path:
        torch_dtype = (
            model_args.torch_dtype
            if model_args.torch_dtype in ["auto", None]
            else getattr(torch, model_args.torch_dtype)
        )
        from_pretrained_kwargs = {
            "from_tf": bool(".ckpt" in model_args.model_name_or_path),
            "config": config,
            "cache_dir": model_args.cache_dir,
            "revision": model_args.model_revision,
            "use_auth_token": True if model_args.use_auth_token else None,
            "torch_dtype": torch_dtype,
            "low_cpu_mem_usage": model_args.low_cpu_mem_usage,
        }
        if model_args.use_flash_attention_2:
            check_min_version("4.34.0")
            from_pretrained_kwargs["use_flash_attention_2"] = model_args.use_flash_attention_2
        model = AutoModelForCausalLM.from_pretrained(
            model_args.model_name_or_path,
            trust_remote_code=True,
            ignore_mismatched_sizes=True,
            **from_pretrained_kwargs,
        )
    else:
        model = AutoModelForCausalLM.from_config(
            config,
            trust_remote_code=True,
        )
        n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
        logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")

    if peft_args.peft_type is not None:
        peft_config = get_peft_config(peft_args)
        logger.info(f"Load Peft {peft_args.peft_type} model ......")
        if training_args.gradient_checkpointing and peft_config.peft_type in ["lora", "qlora"]:
            # Make Lora and gradient checkpointing compatible
            # https://github.com/huggingface/peft/issues/137
            model.enable_input_require_grads()
        model = get_peft_model(model, peft_config)

    if model_args.use_bettertransformer_kernels:
        try:
            from optimum.bettertransformer import BetterTransformer

            model = BetterTransformer.transform(model)
        except ImportError:
            print("Module 'optimum' not found. Please install package by 'pip install optimum'")
    return model


def get_lm_datasets(data_args, tokenized_datasets, tokenizer, training_args):

    if data_args.block_size is None:
        block_size = tokenizer.model_max_length
        if block_size > 1024:
            logger.warning(
                "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
                " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
                " override this default with `--block_size xxx`."
            )
            block_size = 1024
    else:
        if data_args.block_size > tokenizer.model_max_length:
            logger.warning(
                f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
                f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
            )
        block_size = min(data_args.block_size, tokenizer.model_max_length)

    # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
    def group_texts(examples):
        # Concatenate all texts.
        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
        total_length = len(concatenated_examples[list(examples.keys())[0]])
        # We drop the small remainder, and if the total_length < block_size  we exclude this batch and return an empty
        # dict. We could add padding if the model supported it instead of this drop, you can customize this part to
        # your needs.
        total_length = (total_length // block_size) * block_size
        # Split by chunks of max_len.
        result = {
            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
            for k, t in concatenated_examples.items()
        }
        result["labels"] = result["input_ids"].copy()
        return result

    # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
    # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
    # to preprocess.
    #
    # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
    # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map

    with training_args.main_process_first(desc="grouping texts together"):
        if not data_args.streaming:
            lm_datasets = tokenized_datasets.map(
                group_texts,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=not data_args.overwrite_cache,
                desc=f"Grouping texts in chunks of {block_size}",
            )
        else:
            lm_datasets = tokenized_datasets.map(
                group_texts,
                batched=True,
            )
    return lm_datasets


def get_raw_datasets(data_args, model_args):

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
            streaming=data_args.streaming,
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
                streaming=data_args.streaming,
            )
            raw_datasets["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
                streaming=data_args.streaming,
            )
    elif data_args.dataset_path is not None:
        raw_datasets = load_from_disk(data_args.dataset_path)
    else:
        data_files = {}
        dataset_args = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = (
            data_args.train_file.split(".")[-1]
            if data_args.train_file is not None
            else data_args.validation_file.split(".")[-1]
        )
        if extension == "txt":
            extension = "text"
            dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
            **dataset_args,
        )
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
                **dataset_args,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
                **dataset_args,
            )

    return raw_datasets


def tokenize_raw_datasets(raw_datasets, tokenizer, data_args, training_args):
    if training_args.do_train:
        column_names = list(raw_datasets["train"].features)
    else:
        column_names = list(raw_datasets["validation"].features)
    text_column_name = "text" if "text" in column_names else column_names[0]

    # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
    tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")

    def tokenize_function(examples):
        with CaptureLogger(tok_logger) as cl:
            output = tokenizer(examples[text_column_name])
        # clm input could be much much longer than block_size
        if "Token indices sequence length is longer than the" in cl.out:
            tok_logger.warning(
                "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
                " before being passed to the model."
            )
        return output

    with training_args.main_process_first(desc="dataset map tokenization"):
        if not data_args.streaming:
            tokenized_datasets = raw_datasets.map(
                tokenize_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on dataset",
            )
        else:
            tokenized_datasets = raw_datasets.map(
                tokenize_function,
                batched=True,
                remove_columns=column_names,
            )
    return tokenized_datasets


def get_peft_config(peft_args):
    """
    Returns:
        config(PeftConfig)
    """
    if peft_args.peft_type == "lora":
        peft_config = LoraConfig(
            task_type=peft_args.peft_task_type,
            inference_mode=False,
            r=peft_args.lora_r,
            lora_alpha=peft_args.lora_alpha,
            lora_dropout=peft_args.lora_dropout,
            target_modules=peft_args.lora_target_modules,
        )
    else:
        raise NotImplementedError(f"Not support {peft_args.peft_type}")
    return peft_config


class TorchProfCallback(TrainerCallback):
    def __init__(self, prof):
        self.prof = prof

    def on_step_end(self, args, state, control, **kwargs):
        self.prof.step()


# for auto_accelerate
def optim_param_func(model, args):
    no_decay = ["bias", "LlamaRMSNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            "weight_decay": args["weight_decay"],
        },
        {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
    ]
    return optimizer_grouped_parameters


def compute_training_flops(
    batch_size,
    sequence_length,
    hidden_size,
    vocab_size,
    intermediate_size,
    num_layers,
    use_gradient_checkpointing=False,
    use_peft=False,
):
    """Returns:
    hardware flops
    model flops

    The source of formula:
    Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM's
    (APPENDIX: FLOATING-POINT OPERATIONS)

    Assuming that backward pass has twice FLOPs as many as forward pass. Only matrix multiplication FLOPs are computed.
    For use_peft, backward pass FLOPS is a little more than the forward pass. Assuming equal for simplicity here.
    """
    attention_forward_flops = (
        8 * batch_size * sequence_length * hidden_size**2 + 4 * batch_size * sequence_length**2 * hidden_size
    )
    # llama2 use gate_proj, has 3 Linears
    two_mlps_forward_flops = 3 * 2 * batch_size * sequence_length * hidden_size * intermediate_size
    logits_forward_flops = 2 * batch_size * sequence_length * hidden_size * vocab_size
    decoder_layer_forward_flops = attention_forward_flops + two_mlps_forward_flops
    # forward FLOPs without gradient checkpointing
    forward_flops_wo_gc = num_layers * decoder_layer_forward_flops + logits_forward_flops
    factor = 2 if use_peft else 3
    if not use_gradient_checkpointing:
        return forward_flops_wo_gc * factor, forward_flops_wo_gc * factor
    else:
        return (
            num_layers * decoder_layer_forward_flops * (factor + 1) + logits_forward_flops * factor,
            forward_flops_wo_gc * factor,
        )


class MyTensorBoard(TensorBoardCallback):
    def __init__(self, flops_per_gpu_per_iteration, tb_writer=None):
        super().__init__(tb_writer)
        self.flops_per_gpu_per_iteration = flops_per_gpu_per_iteration
        self.start = 0
        self.end = 0

    def on_step_begin(self, args, state, control, **kwargs):
        torch.cuda.synchronize()
        self.start = time.time()

    def on_step_end(self, args, state, control, **kwargs):
        torch.cuda.synchronize()
        self.end = time.time()

    def on_log(self, args, state, control, logs=None, **kwargs):
        cost_per_iter = self.end - self.start
        flops_per_sec = self.flops_per_gpu_per_iteration / cost_per_iter if cost_per_iter > 0 else 0
        logs["TFLOPS"] = int(flops_per_sec / 1e12)
        return super().on_log(args, state, control, logs, **kwargs)


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, LlamaTrainingArguments, PeftArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, peft_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, peft_args = parser.parse_args_into_dataclasses()

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        files_in_output_dir = os.listdir(training_args.output_dir)
        if last_checkpoint is None and len(files_in_output_dir) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                f"Use --overwrite_output_dir to overcome. `ls {training_args.output_dir}` is {files_in_output_dir}."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = get_config(model_args)
    tokenizer = get_tokenizer(model_args)

    if last_checkpoint and training_args.save_load_by_streaming:
        streaming_ckpt_path = os.path.join(last_checkpoint, STREAMING_CKPT_DIR)
        if os.path.exists(streaming_ckpt_path):
            with init_empty_weights_with_disk_offload(ckpt_path=streaming_ckpt_path):
                model = LlamaForCausalLM(config)
        else:
            logger.warning(f"Can't find stream model {streaming_ckpt_path}.")
            model = get_model(model_args, training_args, peft_args, config)
    else:
        model = get_model(model_args, training_args, peft_args, config)

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    if "alpaca" in data_args.dataset_path:
        if training_args.do_train:
            train_dataset = InstructionDataset(
                data_args.dataset_path,
                tokenizer,
                partition="train",
                max_words=data_args.block_size,
            )
        if training_args.do_eval:
            eval_dataset = InstructionDataset(
                data_args.dataset_path,
                tokenizer,
                partition="eval",
                max_words=data_args.block_size,
            )
    else:
        # See more about loading any type of standard or custom dataset (from files, python dict,
        # pandas DataFrame, etc) at https://huggingface.co/docs/datasets/loading_datasets.html.
        raw_datasets = get_raw_datasets(data_args, model_args)

        # Preprocessing the datasets.
        # First we tokenize all the texts.
        tokenized_datasets = tokenize_raw_datasets(raw_datasets, tokenizer, data_args, training_args)

        lm_datasets = get_lm_datasets(data_args, tokenized_datasets, tokenizer, training_args)

        if training_args.do_train:
            if "train" not in tokenized_datasets:
                raise ValueError("--do_train requires a train dataset")
            train_dataset = lm_datasets["train"]
            if data_args.max_train_samples is not None:
                max_train_samples = min(len(train_dataset), data_args.max_train_samples)
                train_dataset = train_dataset.select(range(max_train_samples))

        if training_args.do_eval:
            if "validation" not in tokenized_datasets:
                raise ValueError("--do_eval requires a validation dataset")
            eval_dataset = lm_datasets["validation"]
            if data_args.max_eval_samples is not None:
                max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
                eval_dataset = eval_dataset.select(range(max_eval_samples))

            def preprocess_logits_for_metrics(logits, labels):
                if isinstance(logits, tuple):
                    # Depending on the model and config, logits may contain extra tensors,
                    # like past_key_values, but logits always come first
                    logits = logits[0]
                return logits.argmax(dim=-1)

            metric = evaluate.load(data_args.evaluate_script)

            def compute_metrics(eval_preds: EvalPrediction):
                preds, labels = eval_preds
                # preds have the same shape as the labels, after the argmax(-1) has been calculated
                # by preprocess_logits_for_metrics but we need to shift the labels
                labels = labels[:, 1:].reshape(-1)
                preds = preds[:, :-1].reshape(-1)
                return metric.compute(predictions=preds, references=labels)

    training_args.atorch_wrap_cls = (LlamaDecoderLayer,)
    training_args.atorch_checkpoint_cls = (LlamaDecoderLayer,)
    training_args.model_input_format = "unpack_dict"
    # training_args.optim_param_func = optim_param_func

    # Initialize our Trainer
    trainer = AtorchTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        optimizers=(torch.optim.AdamW, None),
        data_collator=default_data_collator,
        compute_metrics=compute_metrics
        if training_args.do_eval and not is_torch_tpu_available() and "alpaca" not in data_args.dataset_path
        else None,
        preprocess_logits_for_metrics=preprocess_logits_for_metrics
        if training_args.do_eval and not is_torch_tpu_available() and "alpaca" not in data_args.dataset_path
        else None,
    )

    flops_per_gpu_per_iteration, _ = compute_training_flops(
        training_args.per_device_train_batch_size,
        data_args.block_size,
        config.hidden_size,
        config.vocab_size,
        config.intermediate_size,
        config.num_hidden_layers,
        training_args.gradient_checkpointing,
        peft_args.peft_type is not None,
    )
    trainer.add_callback(MyTensorBoard(flops_per_gpu_per_iteration))

    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        with torch.profiler.profile(
            schedule=torch.profiler.schedule(wait=1, warmup=1, active=1, repeat=1, skip_first=3),
            on_trace_ready=torch.profiler.tensorboard_trace_handler(
                os.path.join(training_args.output_dir, "torch_profile")
            ),
            activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
            profile_memory=True,
            with_stack=True,
            with_modules=True,
            record_shapes=True,
        ) as prof:
            if training_args.enable_torch_profiler:
                trainer.add_callback(TorchProfCallback(prof=prof))
            train_result = trainer.train(resume_from_checkpoint=checkpoint)
        # trainer.save_model()  # Saves the tokenizer too for easy upload

        metrics = train_result.metrics

        max_train_samples = (
            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
        )
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval and "alpaca" not in data_args.dataset_path:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate()

        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
        try:
            perplexity = math.exp(metrics["eval_loss"])
        except OverflowError:
            perplexity = float("inf")
        metrics["perplexity"] = perplexity

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
    if data_args.dataset_name is not None:
        kwargs["dataset_tags"] = data_args.dataset_name
        if data_args.dataset_config_name is not None:
            kwargs["dataset_args"] = data_args.dataset_config_name
            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
        else:
            kwargs["dataset"] = data_args.dataset_name


if __name__ == "__main__":
    main()
